Animal models used in psychiatric drug discovery are often developed strictly for their predictive validity. The main purpose of such a model typically is to predict the neuropharmacological properties of novel compounds with a moderate to high degree of sensitivity and specificity. The behavioral endpoint (measure) may or may not be developed with specific regard to the model's face or construct validity. Ideally, an animal model of this nature is amenable to relatively high through-put and focus on behaviors that have the following properties: i) algorithmically definable and automatically measurable; ii) sufficiently common in natural behavior to supply large samples; iii) sufficiently complex to provide a relatively detailed profile of a drug's psychoactive properties (especially those unique to the drug class); iv) resistant to minor environmental changes; and v) replicable across laboratories (i.e., determined largely through genetics and not environment).
In conventional behavioral drug discovery studies, a drug is administered to a test subject, for example, an animal such as a laboratory mouse. The drug-injected test subject is then subjected to a battery of tests. Tests can be specific to a particular behavior or more generic in nature. For example, a test may be specific to measuring anxiety or learning. However, such specific tests are difficult, expensive, and labor intensive to carry out. Tests that are more general in nature, such a measuring increased activity, are easier and less expensive to carry out. However, general tests are less informative.
Many of the currently employed standard behavioral tests in pre-clinical and basic research automatically record large amounts of information-rich data. The application of bioinformatics paradigms, such as exploratory data analysis and data mining, would appear well suited to be employed with such large amounts of data in order to provide further behavioral information. Unfortunately, in most current behavioral tests these data are rarely explored or mined, and are usually used merely for calculating a small set of hardwired cumulative measures, which may fail to detect subtle behavioral effects in knockouts and transgenics (e.g., Grammer, M., Kuchay, S, Chishti, A. & Baudry, M. (2005) Lack of phenotype for LTP and fear conditioning learning in calpain 1 knock-out mice. Neurobiol Learn Mem 84(3), 222-227, which is herein incorporated by reference in its entirety; Perez, F. A. & Palmiter, R. D. (2005) Parkin-deficient mice are not a robust model of parkinsonism. Proc Natl Acad Sci USA 102(6), 2174-2179, which is herein incorporated by reference in its entirety) or effects of genetic manipulation. Therefore, it would be desirable to provide a behavioral testing paradigm for mining and analyzing large amounts of behavioral data using the large number of measures from the testing to isolate subtle and consistent behavioral effects or effects of genetic manipulation.
Behavioral testing that has been employed for the SOD1G93A (SOD1) rat model of Amyotrophic Lateral Sclerosis (ALS), is an example of the above problem. Transgenic rats and mice expressing any of several mutant human SOD1 alleles show many features of human ALS, including adult-onset muscle weakness as well as severe motor neuron loss (Gurney, M. E., Pu, H., Chiu, A. Y., Dal Canto, M. C., Polchow, C. Y., Alexander, D. D., Caliendo, J., Hentati, A., Kwon, Y. W., Deng, H. X. et al. (1994) Motor neuron degeneration in mice that express a human Cu,Zn superoxide dismutase mutation. Science 264(5166), 1772-1775, which is herein incorporated by reference in its entirety; Bruijn, L. I., Miller, T. M. & Cleveland, D. W. (2004) Unraveling the mechanisms involved in motor neuron degeneration in ALS. Annu Rev Neurosci 27, 723-749, which is herein incorporated by reference in its entirety) usually culminating in death by four months of age. These genetic models are widely used for developing and testing treatments (Howland, D. S., Liu, J., She, Y., Goad, B., Maragakis, N. J., Kim, B., Erickson, J., Kulik, J., DeVito, L., Psaltis, G., DeGennaro, L. J., Cleveland, D. W. & Roth-stein, J. D., (2002). Focal loss of the glutamate transporter EAAT2 in a transgenic rat model of SOD1 mutant-mediated amyotrophic lateral sclerosis (ALS). Proc Natl Acad Sci USA 99, 1604-1609, which is herein incorporated by reference in its entirety; Rothstein, J. D., Patel, S., Regan, M. R., Haenggeli, C., Huang, Y. H., Bergles, D. E., Jin, L, Dykes Hoberg, M., Vidensky, S., Chung, D. S., Toan, S. V., Bruijn, L, I, Su, Z. Z., Gupta, P. & Fisher P B. (2005) Beta-lactam antibiotics offer neuroprotection by increasing glutamate transporter expression. Nature 433, 73-77, which is herein incorporated by reference in its entirety). In SOD1 rats the well-described adult-onset of the disease typically occurs around post-natal day (PND) 110. Discovery of putative earlier motor symptoms that could be measured, in any manner including automatically, and reliably in younger animals may enable investigators to develop and test treatments for delaying or even preventing the disease. Moreover, such symptoms may prove useful for contrasting symptomalogies with non-genetic animal models of ALS (Shaw, C. A. & Wilson, J. M. B. (2006) Environmental toxicity and ALS: novel ilnsights from an animal model of ALS-PDC, in Amyotrophic Lateral Sclerosis. Mitsumoto, H., Przedborski, S. and Gordon, P. H., (Eds), New York: Taylor & Francis, 435-448, which is herein incorporated by reference in its entirety). Unfortunately, such early symptoms have not been found by the current behavioral tests being employed. Matsumoto et al., 2006, (Matsumoto, A., Okada, Y., Nakamichi, M., Nakamura, M., Toyama, Y., Sobue, G., Nagai, M., Aoki, M., Itoyama, Y. & Okano, H. (2006) Disease progression of human SOD1 (G93A) transgenic ALS model rats. J Neurosci Res 83 (1), 119-33, which is herein incorporated by reference in its entirety), has recently phenotyped SOD1 mutant rats using several behavioral tests, including righting reflex, inclined plane (for testing grip strength), home-cage and open-field activity, but failed to detect reliable symptoms before PND 100. Moreover, Matsumoto et al., 2006, failed to detect any abnormality in these animals before PND 90 by subjective observations of their behavior. Therefore, it would be desirable to provide a paradigm capable of screening numerous behavioral patterns in order to isolate reliable differences, such as premorbid (<PND 90) in the current example, in behavioral patterns between diseased subjects and normal subjects. It would be further desirable to utilize these reliable differences in behavioral patterns for contrasting symptomalogies with non-genetic animal models of various diseases.
The example above further illustrates another typical problem with the current behavioral test methodologies being employed, wherein in animal models, the most immediate hypotheses regarding the behavioral effect of the mutation were already exhausted. Using the standard behavioral testing models, the next step may be the testing of a more elaborate hypotheses, in a one-by-one manner using dedicated (and likely costly and time-consuming) setups with an unknown chance of success. Therefore, it would be desirable to provide a behavioral testing paradigm capable of more effectively utilizing the wealth of dynamical motor pattern information, typically collected from simple open-field test of these animals, that to date have mostly been ignored.
Another problem with prior methodologies using animal behavioral models is that the results may be laboratory or experimenter dependent. That is, different results may be obtained simply because of where the testing is performed or who performs the test. Therefore, it would be desirable to provide a behavioral testing paradigm capable of providing more consistent and reliable results regardless of where the testing is performed or who performs the test.
Medications for treatment intervention in drug abuse are currently a significant need for many thousands, if not millions, of people around the world. In addition to the toll drug addiction takes on the individual and those closest, drug addiction costs billions of dollars in direct and indirect health care resources. These costs often being passed on to others through higher premiums. Significant advances in our understanding of the neural mechanisms that underlie drug-taking behavior have been made. Unfortunately, similar advances in developing pharmacotherapeutic interventions have yet to be realized, especially in psychomotor stimulant abuse. Therefore it would be desirable to provide a system and method for discovering the psychopharmacological properties of a durg and to apply it to the study of novel therapeutic agents.